Overview

Dataset statistics

Number of variables27
Number of observations827
Missing cells74
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory174.6 KiB
Average record size in memory216.2 B

Variable types

NUM19
CAT8

Warnings

name has a high cardinality: 195 distinct values High cardinality
saifi_nomed has a high cardinality: 472 distinct values High cardinality
caidi_nomed has a high cardinality: 791 distinct values High cardinality
gen_mwh has a high cardinality: 402 distinct values High cardinality
dem_res_mwh has a high cardinality: 334 distinct values High cardinality
total_cust is highly correlated with total_mwhHigh correlation
total_mwh is highly correlated with total_custHigh correlation
nm_mwh is highly correlated with pv_mwhHigh correlation
pv_mwh is highly correlated with nm_mwhHigh correlation
nm_pct is highly correlated with pv_pctHigh correlation
pv_pct is highly correlated with nm_pctHigh correlation
voltage has 73 (8.8%) missing values Missing
wind_mwh is highly skewed (γ1 = 20.1350571) Skewed
purchase_mwh has unique values Unique
voltage has 378 (45.7%) zeros Zeros
pv_mwh has 400 (48.4%) zeros Zeros
wind_mwh has 609 (73.6%) zeros Zeros
nm_mwh has 396 (47.9%) zeros Zeros
ee_mwh has 35 (4.2%) zeros Zeros
dem_res_customers has 24 (2.9%) zeros Zeros
pv_pct has 400 (48.4%) zeros Zeros
wind_pct has 609 (73.6%) zeros Zeros
nm_pct has 396 (47.9%) zeros Zeros
ee_pct has 35 (4.2%) zeros Zeros
dem_res_cust_pct has 25 (3.0%) zeros Zeros
circuit_pct has 451 (54.5%) zeros Zeros

Reproduction

Analysis started2020-12-18 04:57:11.257485
Analysis finished2020-12-18 04:58:42.162922
Duration1 minute and 30.91 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

year
Real number (ℝ≥0)

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.131802
Minimum2013
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size6.5 KiB
2020-12-17T23:58:42.318356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12014
median2016
Q32018
95-th percentile2019
Maximum2019
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.99230773
Coefficient of variation (CV)0.0009881832767
Kurtosis-1.25966747
Mean2016.131802
Median Absolute Deviation (MAD)2
Skewness-0.05083934622
Sum1667341
Variance3.969290091
MonotocityDecreasing
2020-12-17T23:58:42.442678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
201913316.1%
 
201412815.5%
 
201812314.9%
 
201611914.4%
 
201711814.3%
 
201511013.3%
 
20139611.6%
 
ValueCountFrequency (%) 
20139611.6%
 
201412815.5%
 
201511013.3%
 
201611914.4%
 
201711814.3%
 
ValueCountFrequency (%) 
201913316.1%
 
201812314.9%
 
201711814.3%
 
201611914.4%
 
201511013.3%
 

name
Categorical

HIGH CARDINALITY

Distinct195
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Tri-County Electric Coop
 
9
San Diego Gas & Electric Co
 
7
PECO Energy Co
 
7
North Arkansas Elec Coop, Inc
 
7
Ohio Edison Co
 
7
Other values (190)
790 
ValueCountFrequency (%) 
Tri-County Electric Coop91.1%
 
San Diego Gas & Electric Co70.8%
 
PECO Energy Co70.8%
 
North Arkansas Elec Coop, Inc70.8%
 
Ohio Edison Co70.8%
 
Southern California Edison Co70.8%
 
Sierra Pacific Power Co70.8%
 
Louisville Gas & Electric Co70.8%
 
Lyon Rural Electric Coop70.8%
 
Northern States Power Co - Minnesota70.8%
 
Other values (185)75591.3%
 
2020-12-17T23:58:42.641238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique40 ?
Unique (%)4.8%
2020-12-17T23:58:43.167845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length39
Median length26
Mean length25.70858525
Min length12

state
Categorical

Distinct41
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
MN
85 
WI
 
51
NC
 
49
IN
 
42
SC
 
41
Other values (36)
559 
ValueCountFrequency (%) 
MN8510.3%
 
WI516.2%
 
NC495.9%
 
IN425.1%
 
SC415.0%
 
TX415.0%
 
AZ394.7%
 
CA384.6%
 
OH354.2%
 
GA344.1%
 
Other values (31)37245.0%
 
2020-12-17T23:58:43.843565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.2%
2020-12-17T23:58:44.030249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

total_mwh
Real number (ℝ≥0)

HIGH CORRELATION

Distinct826
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9345694.03
Minimum57432
Maximum111955723
Zeros0
Zeros (%)0.0%
Memory size6.5 KiB
2020-12-17T23:58:44.204500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum57432
5-th percentile135136.2
Q1502877.5
median2327987
Q312053663
95-th percentile36982188.2
Maximum111955723
Range111898291
Interquartile range (IQR)11550785.5

Descriptive statistics

Standard deviation15836364.06
Coefficient of variation (CV)1.694509152
Kurtosis13.56327341
Mean9345694.03
Median Absolute Deviation (MAD)2104514
Skewness3.251402801
Sum7728888963
Variance2.507904267e+14
MonotocityNot monotonic
2020-12-17T23:58:44.529292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
34526920.2%
 
1828454310.1%
 
7355410.1%
 
843216010.1%
 
5943410.1%
 
107039810.1%
 
19748210.1%
 
35566210.1%
 
72770610.1%
 
24746210.1%
 
Other values (816)81698.7%
 
ValueCountFrequency (%) 
5743210.1%
 
5915110.1%
 
5938010.1%
 
5943410.1%
 
6012510.1%
 
ValueCountFrequency (%) 
11195572310.1%
 
11007276010.1%
 
10944914410.1%
 
10851359410.1%
 
10443109610.1%
 

total_cust
Real number (ℝ≥0)

HIGH CORRELATION

Distinct824
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean465552.5139
Minimum2109
Maximum5236724
Zeros0
Zeros (%)0.0%
Memory size6.5 KiB
2020-12-17T23:58:44.722857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2109
5-th percentile6722.5
Q124274
median95602
Q3512497.5
95-th percentile1865335.1
Maximum5236724
Range5234615
Interquartile range (IQR)488223.5

Descriptive statistics

Standard deviation851224.1907
Coefficient of variation (CV)1.828417129
Kurtosis14.4043783
Mean465552.5139
Median Absolute Deviation (MAD)85649
Skewness3.490218705
Sum385011929
Variance7.245826228e+11
MonotocityNot monotonic
2020-12-17T23:58:45.136915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
130347330.4%
 
14503320.2%
 
7475110.1%
 
15721110.1%
 
15172210.1%
 
21008910.1%
 
633410.1%
 
5623410.1%
 
70108510.1%
 
273058810.1%
 
Other values (814)81498.4%
 
ValueCountFrequency (%) 
210910.1%
 
213210.1%
 
216310.1%
 
218910.1%
 
221110.1%
 
ValueCountFrequency (%) 
523672410.1%
 
518830810.1%
 
506918910.1%
 
506148310.1%
 
502087610.1%
 

no
Real number (ℝ≥0)

Distinct183
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12348.36155
Minimum329
Maximum55937
Zeros0
Zeros (%)0.0%
Memory size6.5 KiB
2020-12-17T23:58:45.600907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum329
5-th percentile1015
Q15780.5
median13664
Q316945.5
95-th percentile20885
Maximum55937
Range55608
Interquartile range (IQR)11165

Descriptive statistics

Standard deviation7549.575265
Coefficient of variation (CV)0.6113827519
Kurtosis7.563501311
Mean12348.36155
Median Absolute Deviation (MAD)4616
Skewness1.412761106
Sum10212095
Variance56996086.68
MonotocityNot monotonic
2020-12-17T23:58:45.848035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1716670.8%
 
1949070.8%
 
80370.8%
 
1129870.8%
 
1998170.8%
 
919170.8%
 
1432870.8%
 
1367670.8%
 
1124970.8%
 
1494070.8%
 
Other values (173)75791.5%
 
ValueCountFrequency (%) 
32960.7%
 
40710.1%
 
55410.1%
 
59070.8%
 
63610.1%
 
ValueCountFrequency (%) 
5593770.8%
 
2693930.4%
 
2529550.6%
 
2494970.8%
 
2421160.7%
 

type
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Cooperative
362 
Investor Owned
335 
Municipal
99 
Political Subdivision
 
24
State
 
7
ValueCountFrequency (%) 
Cooperative36243.8%
 
Investor Owned33540.5%
 
Municipal9912.0%
 
Political Subdivision242.9%
 
State70.8%
 
2020-12-17T23:58:46.042928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-17T23:58:46.161575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:46.342830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length11
Mean length12.21523579
Min length5

saidi_nomed
Real number (ℝ≥0)

Distinct766
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.247237
Minimum0
Maximum812.9
Zeros1
Zeros (%)0.1%
Memory size6.5 KiB
2020-12-17T23:58:46.508722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.605
Q159.49
median89.01
Q3133.65
95-th percentile218.9342
Maximum812.9
Range812.9
Interquartile range (IQR)74.16

Descriptive statistics

Standard deviation73.23225751
Coefficient of variation (CV)0.7024863163
Kurtosis16.73926098
Mean104.247237
Median Absolute Deviation (MAD)34.15
Skewness2.850259763
Sum86212.465
Variance5362.963539
MonotocityNot monotonic
2020-12-17T23:58:46.699268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14350.6%
 
8140.5%
 
13840.5%
 
9330.4%
 
58.830.4%
 
7130.4%
 
9530.4%
 
10630.4%
 
158.430.4%
 
9430.4%
 
Other values (756)79395.9%
 
ValueCountFrequency (%) 
010.1%
 
3.02310.1%
 
4.44510.1%
 
4.8310.1%
 
6.310.1%
 
ValueCountFrequency (%) 
812.910.1%
 
599.76510.1%
 
574.82210.1%
 
490.68610.1%
 
421.410.1%
 

saifi_nomed
Categorical

HIGH CARDINALITY

Distinct472
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
.
 
30
1
 
11
0.87
 
11
1.17
 
7
0.58
 
7
Other values (467)
761 
ValueCountFrequency (%) 
.303.6%
 
1111.3%
 
0.87111.3%
 
1.1770.8%
 
0.5870.8%
 
1.0670.8%
 
0.7960.7%
 
0.8260.7%
 
0.6660.7%
 
0.960.7%
 
Other values (462)73088.3%
 
2020-12-17T23:58:46.922115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique333 ?
Unique (%)40.3%
2020-12-17T23:58:47.130711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length4.188633615
Min length1

caidi_nomed
Categorical

HIGH CARDINALITY

Distinct791
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
.
 
31
100
 
4
44.155844
 
2
64
 
2
144.44444
 
2
Other values (786)
786 
ValueCountFrequency (%) 
.313.7%
 
10040.5%
 
44.15584420.2%
 
6420.2%
 
144.4444420.2%
 
103.1446510.1%
 
112.5757610.1%
 
92.510.1%
 
74.14117610.1%
 
92.74725310.1%
 
Other values (781)78194.4%
 
2020-12-17T23:58:47.335921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique786 ?
Unique (%)95.0%
2020-12-17T23:58:47.530315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length8.269649335
Min length1

circuits
Real number (ℝ≥0)

Distinct428
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean579.2478839
Minimum5
Maximum5619
Zeros0
Zeros (%)0.0%
Memory size6.5 KiB
2020-12-17T23:58:47.717764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile26
Q165
median178
Q3759
95-th percentile2334.1
Maximum5619
Range5614
Interquartile range (IQR)694

Descriptive statistics

Standard deviation922.2076039
Coefficient of variation (CV)1.59207764
Kurtosis9.403079463
Mean579.2478839
Median Absolute Deviation (MAD)140
Skewness2.83717254
Sum479038
Variance850466.8646
MonotocityNot monotonic
2020-12-17T23:58:47.892979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
87121.5%
 
27121.5%
 
40121.5%
 
68111.3%
 
36111.3%
 
6491.1%
 
2091.1%
 
6091.1%
 
7481.0%
 
6281.0%
 
Other values (418)72687.8%
 
ValueCountFrequency (%) 
560.7%
 
710.1%
 
1110.1%
 
1610.1%
 
1810.1%
 
ValueCountFrequency (%) 
561910.1%
 
559610.1%
 
552520.2%
 
548420.2%
 
546410.1%
 

voltage
Real number (ℝ≥0)

MISSING
ZEROS

Distinct182
Distinct (%)24.1%
Missing73
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean158.5
Minimum0
Maximum2869
Zeros378
Zeros (%)45.7%
Memory size6.5 KiB
2020-12-17T23:58:48.079910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q373
95-th percentile1029.05
Maximum2869
Range2869
Interquartile range (IQR)73

Descriptive statistics

Standard deviation422.4063622
Coefficient of variation (CV)2.665024367
Kurtosis17.22259943
Mean158.5
Median Absolute Deviation (MAD)0
Skewness3.934946187
Sum119509
Variance178427.1348
MonotocityNot monotonic
2020-12-17T23:58:48.259325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
037845.7%
 
1131.6%
 
44121.5%
 
10111.3%
 
13101.2%
 
1191.1%
 
3291.1%
 
1281.0%
 
670.8%
 
470.8%
 
Other values (172)29035.1%
 
(Missing)738.8%
 
ValueCountFrequency (%) 
037845.7%
 
1131.6%
 
210.1%
 
340.5%
 
470.8%
 
ValueCountFrequency (%) 
286910.1%
 
278710.1%
 
278610.1%
 
276110.1%
 
274010.1%
 

nerc
Categorical

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
MRO
198 
SERC
183 
RFC
159 
WECC
144 
SPP
56 
Other values (5)
87 
ValueCountFrequency (%) 
MRO19823.9%
 
SERC18322.1%
 
RFC15919.2%
 
WECC14417.4%
 
SPP566.8%
 
NPCC293.5%
 
TRE293.5%
 
FRCC273.3%
 
MISO10.1%
 
HI10.1%
 
2020-12-17T23:58:48.449889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.2%
2020-12-17T23:58:48.573287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:48.931006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.46311971
Min length2

gen_mwh
Categorical

HIGH CARDINALITY

Distinct402
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
.
329 
0.0
68 
0
 
30
59
 
2
10615897
 
1
Other values (397)
397 
ValueCountFrequency (%) 
.32939.8%
 
0.0688.2%
 
0303.6%
 
5920.2%
 
1061589710.1%
 
369221110.1%
 
1517516110.1%
 
1081732110.1%
 
1521920010.1%
 
1336381710.1%
 
Other values (392)39247.4%
 
2020-12-17T23:58:49.446883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique398 ?
Unique (%)48.1%
2020-12-17T23:58:49.825391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length4.160822249
Min length1

purchase_mwh
Real number (ℝ≥0)

UNIQUE

Distinct827
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4911936.447
Minimum61207
Maximum96778091
Zeros0
Zeros (%)0.0%
Memory size6.5 KiB
2020-12-17T23:58:50.081164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum61207
5-th percentile149341.2
Q1514131
median1565619
Q35369209
95-th percentile19402625.8
Maximum96778091
Range96716884
Interquartile range (IQR)4855078

Descriptive statistics

Standard deviation9119144.441
Coefficient of variation (CV)1.856527367
Kurtosis31.12538555
Mean4911936.447
Median Absolute Deviation (MAD)1328027
Skewness4.801381023
Sum4062171442
Variance8.315879533e+13
MonotocityNot monotonic
2020-12-17T23:58:50.278407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
167841010.1%
 
305019710.1%
 
34544410.1%
 
232936610.1%
 
61101210.1%
 
21795610.1%
 
84038610.1%
 
431463810.1%
 
14968010.1%
 
709989410.1%
 
Other values (817)81798.8%
 
ValueCountFrequency (%) 
6120710.1%
 
6157110.1%
 
6265810.1%
 
6267110.1%
 
6280210.1%
 
ValueCountFrequency (%) 
9677809110.1%
 
7229353710.1%
 
7048719510.1%
 
7007862110.1%
 
6937669610.1%
 

pv_mwh
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct423
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2691.103571
Minimum0
Maximum174137.899
Zeros400
Zeros (%)48.4%
Memory size6.5 KiB
2020-12-17T23:58:50.509548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.838
Q3175.45
95-th percentile10182.2472
Maximum174137.899
Range174137.899
Interquartile range (IQR)175.45

Descriptive statistics

Standard deviation14105.40098
Coefficient of variation (CV)5.24149317
Kurtosis76.14927457
Mean2691.103571
Median Absolute Deviation (MAD)0.838
Skewness8.142343136
Sum2225542.653
Variance198962336.9
MonotocityNot monotonic
2020-12-17T23:58:50.711887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
040048.4%
 
17730.4%
 
18120.2%
 
4620.2%
 
0.06820.2%
 
107.2910.1%
 
420.6810.1%
 
229.7110.1%
 
17.13910.1%
 
1046.1710.1%
 
Other values (413)41349.9%
 
ValueCountFrequency (%) 
040048.4%
 
0.00210.1%
 
0.01310.1%
 
0.03210.1%
 
0.03810.1%
 
ValueCountFrequency (%) 
174137.89910.1%
 
169882.50910.1%
 
139279.70110.1%
 
114205.75610.1%
 
100497.16710.1%
 

wind_mwh
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct208
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.81987062
Minimum0
Maximum11648.213
Zeros609
Zeros (%)73.6%
Memory size6.5 KiB
2020-12-17T23:58:51.002083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.016
95-th percentile67.9541
Maximum11648.213
Range11648.213
Interquartile range (IQR)0.016

Descriptive statistics

Standard deviation526.8190703
Coefficient of variation (CV)13.57086106
Kurtosis413.9171896
Mean38.81987062
Median Absolute Deviation (MAD)0
Skewness20.1350571
Sum32104.033
Variance277538.3329
MonotocityNot monotonic
2020-12-17T23:58:51.328354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
060973.6%
 
0.0840.5%
 
0.36230.4%
 
130.4%
 
0.00420.2%
 
0.02820.2%
 
0.00920.2%
 
0.72420.2%
 
1.29910.1%
 
192.76710.1%
 
Other values (198)19823.9%
 
ValueCountFrequency (%) 
060973.6%
 
0.00420.2%
 
0.00510.1%
 
0.00610.1%
 
0.00710.1%
 
ValueCountFrequency (%) 
11648.21310.1%
 
9545.51210.1%
 
1145.00110.1%
 
586.32810.1%
 
574.55310.1%
 

nm_mwh
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct426
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2736.156778
Minimum0
Maximum174137.899
Zeros396
Zeros (%)47.9%
Memory size6.5 KiB
2020-12-17T23:58:51.580254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.855
Q3209.973
95-th percentile10461.6106
Maximum174137.899
Range174137.899
Interquartile range (IQR)209.973

Descriptive statistics

Standard deviation14151.07547
Coefficient of variation (CV)5.171880348
Kurtosis75.08903264
Mean2736.156778
Median Absolute Deviation (MAD)1.855
Skewness8.072683917
Sum2262801.655
Variance200252936.9
MonotocityNot monotonic
2020-12-17T23:58:51.776592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
039647.9%
 
17730.4%
 
14120.2%
 
1620.2%
 
4620.2%
 
0.06820.2%
 
14.62610.1%
 
92.01810.1%
 
1522.75410.1%
 
4130010.1%
 
Other values (416)41650.3%
 
ValueCountFrequency (%) 
039647.9%
 
0.00210.1%
 
0.01310.1%
 
0.03810.1%
 
0.03910.1%
 
ValueCountFrequency (%) 
174137.89910.1%
 
169882.50910.1%
 
139279.70110.1%
 
114205.75610.1%
 
100497.24710.1%
 

ee_mwh
Real number (ℝ≥0)

ZEROS

Distinct767
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118320.0378
Minimum0
Maximum2865573
Zeros35
Zeros (%)4.2%
Memory size6.5 KiB
2020-12-17T23:58:52.034528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.03
Q1589.4
median5739
Q3119200
95-th percentile541983.2
Maximum2865573
Range2865573
Interquartile range (IQR)118610.6

Descriptive statistics

Standard deviation266238.8467
Coefficient of variation (CV)2.250158566
Kurtosis29.20703023
Mean118320.0378
Median Absolute Deviation (MAD)5734.9
Skewness4.592582904
Sum97850671.27
Variance7.088312347e+10
MonotocityNot monotonic
2020-12-17T23:58:52.225805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0354.2%
 
3850.6%
 
8030.4%
 
530.4%
 
6530.4%
 
50920.2%
 
6320.2%
 
12220.2%
 
261320.2%
 
96920.2%
 
Other values (757)76892.9%
 
ValueCountFrequency (%) 
0354.2%
 
0.00210.1%
 
0.0810.1%
 
1.510.1%
 
1.73910.1%
 
ValueCountFrequency (%) 
286557310.1%
 
218004410.1%
 
209742310.1%
 
174760910.1%
 
165510110.1%
 

dem_res_customers
Real number (ℝ≥0)

ZEROS

Distinct662
Distinct (%)80.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean53382.84262
Minimum0
Maximum1323922
Zeros24
Zeros (%)2.9%
Memory size6.5 KiB
2020-12-17T23:58:52.443062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1222
median3807.5
Q323984
95-th percentile378852.75
Maximum1323922
Range1323922
Interquartile range (IQR)23762

Descriptive statistics

Standard deviation158589.3593
Coefficient of variation (CV)2.970792703
Kurtosis24.53806131
Mean53382.84262
Median Absolute Deviation (MAD)3798.5
Skewness4.653784698
Sum44094228
Variance2.515058489e+10
MonotocityNot monotonic
2020-12-17T23:58:53.320242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0242.9%
 
2172.1%
 
1151.8%
 
4131.6%
 
3101.2%
 
2070.8%
 
870.8%
 
1460.7%
 
1150.6%
 
750.6%
 
Other values (652)71786.7%
 
ValueCountFrequency (%) 
0242.9%
 
1151.8%
 
2172.1%
 
3101.2%
 
4131.6%
 
ValueCountFrequency (%) 
132392210.1%
 
124665010.1%
 
122237110.1%
 
117856610.1%
 
113346810.1%
 

dem_res_mwh
Categorical

HIGH CARDINALITY

Distinct334
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
.
177 
0
159 
0.0
68 
 
28
1
 
11
Other values (329)
384 
ValueCountFrequency (%) 
.17721.4%
 
015919.2%
 
0.0688.2%
 
283.4%
 
1111.3%
 
550.6%
 
6240.5%
 
2040.5%
 
4740.5%
 
330.4%
 
Other values (324)36444.0%
 
2020-12-17T23:58:53.624475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique293 ?
Unique (%)35.4%
2020-12-17T23:58:53.885067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length2
Mean length2.347037485
Min length1

pv_pct
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct428
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05855384643
Minimum0
Maximum2.737363978
Zeros400
Zeros (%)48.4%
Memory size6.5 KiB
2020-12-17T23:58:54.075614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.329365372e-05
Q30.01163025297
95-th percentile0.2473360579
Maximum2.737363978
Range2.737363978
Interquartile range (IQR)0.01163025297

Descriptive statistics

Standard deviation0.2444340285
Coefficient of variation (CV)4.17451702
Kurtosis65.28739354
Mean0.05855384643
Median Absolute Deviation (MAD)2.329365372e-05
Skewness7.458000387
Sum48.424031
Variance0.05974799428
MonotocityNot monotonic
2020-12-17T23:58:54.279640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
040048.4%
 
0.00662736569610.1%
 
0.267038638110.1%
 
0.106970502310.1%
 
0.0315200952410.1%
 
0.0883723549410.1%
 
0.0253750851110.1%
 
0.026377448410.1%
 
0.00379104614810.1%
 
0.000956386141110.1%
 
Other values (418)41850.5%
 
ValueCountFrequency (%) 
040048.4%
 
3.16228032e-0710.1%
 
2.673417388e-0610.1%
 
3.142061705e-0610.1%
 
5.819624304e-0610.1%
 
ValueCountFrequency (%) 
2.73736397810.1%
 
2.64836026510.1%
 
2.64636489310.1%
 
2.16369021210.1%
 
1.95657790710.1%
 

wind_pct
Real number (ℝ≥0)

ZEROS

Distinct219
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002713544559
Minimum0
Maximum0.1141962336
Zeros609
Zeros (%)73.6%
Memory size6.5 KiB
2020-12-17T23:58:54.474528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.756518746e-07
95-th percentile0.009655891741
Maximum0.1141962336
Range0.1141962336
Interquartile range (IQR)4.756518746e-07

Descriptive statistics

Standard deviation0.01261911198
Coefficient of variation (CV)4.650416349
Kurtosis37.96209193
Mean0.002713544559
Median Absolute Deviation (MAD)0
Skewness5.927243924
Sum2.24410135
Variance0.0001592419872
MonotocityNot monotonic
2020-12-17T23:58:54.671777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
060973.6%
 
0.000296853295610.1%
 
5.580210277e-0510.1%
 
0.00104681593510.1%
 
0.000492834977110.1%
 
0.0245128079410.1%
 
0.0324039313510.1%
 
0.000167406603910.1%
 
0.00540684708310.1%
 
8.641481499e-0510.1%
 
Other values (209)20925.3%
 
ValueCountFrequency (%) 
060973.6%
 
1.787748248e-0810.1%
 
7.533239162e-0810.1%
 
8.422593326e-0810.1%
 
1.129931644e-0710.1%
 
ValueCountFrequency (%) 
0.114196233610.1%
 
0.109832433910.1%
 
0.105269485810.1%
 
0.100645352310.1%
 
0.0947069717410.1%
 

nm_pct
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct432
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06130904045
Minimum0
Maximum2.737446148
Zeros396
Zeros (%)47.9%
Memory size6.5 KiB
2020-12-17T23:58:54.858947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.853871966e-05
Q30.01433806525
95-th percentile0.2805295462
Maximum2.737446148
Range2.737446148
Interquartile range (IQR)0.01433806525

Descriptive statistics

Standard deviation0.2460776768
Coefficient of variation (CV)4.013725789
Kurtosis63.47212161
Mean0.06130904045
Median Absolute Deviation (MAD)3.853871966e-05
Skewness7.330102662
Sum50.70257646
Variance0.060554223
MonotocityNot monotonic
2020-12-17T23:58:55.041435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
039647.9%
 
0.000724256395310.1%
 
0.000956386141110.1%
 
5.52602057e-0510.1%
 
0.0330492732710.1%
 
0.0461697383110.1%
 
0.142537623910.1%
 
0.0191680136910.1%
 
0.0809159337510.1%
 
0.00192491193310.1%
 
Other values (422)42251.0%
 
ValueCountFrequency (%) 
039647.9%
 
3.16228032e-0710.1%
 
2.673417388e-0610.1%
 
3.142061705e-0610.1%
 
5.819624304e-0610.1%
 
ValueCountFrequency (%) 
2.73744614810.1%
 
2.64863473810.1%
 
2.64640270610.1%
 
2.16645991810.1%
 
1.95657790710.1%
 

ee_pct
Real number (ℝ≥0)

ZEROS

Distinct793
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.074182849
Minimum0
Maximum13.43708864
Zeros35
Zeros (%)4.2%
Memory size6.5 KiB
2020-12-17T23:58:55.228137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0004236764614
Q10.09319228164
median0.5734127815
Q31.268505611
95-th percentile4.121462343
Maximum13.43708864
Range13.43708864
Interquartile range (IQR)1.175313329

Descriptive statistics

Standard deviation1.732035158
Coefficient of variation (CV)1.612421162
Kurtosis15.85436852
Mean1.074182849
Median Absolute Deviation (MAD)0.5289223117
Skewness3.585374758
Sum888.3492162
Variance2.999945789
MonotocityNot monotonic
2020-12-17T23:58:55.434040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0354.2%
 
1.81338638310.1%
 
0.0439788979510.1%
 
1.13418463610.1%
 
0.726592089710.1%
 
1.58761689710.1%
 
3.37581345110.1%
 
1.31763568410.1%
 
0.000987749560910.1%
 
2.23640544210.1%
 
Other values (783)78394.7%
 
ValueCountFrequency (%) 
0354.2%
 
1.333502244e-0610.1%
 
3.820001528e-0510.1%
 
0.000103374202910.1%
 
0.000255295909410.1%
 
ValueCountFrequency (%) 
13.4370886410.1%
 
12.6335389410.1%
 
12.3657116810.1%
 
11.908002210.1%
 
11.114062210.1%
 

dem_res_cust_pct
Real number (ℝ≥0)

ZEROS

Distinct803
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.14745107
Minimum0
Maximum99.99625741
Zeros25
Zeros (%)3.0%
Memory size6.5 KiB
2020-12-17T23:58:55.634188image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001321281614
Q10.2068919757
median7.02880358
Q322.90882002
95-th percentile48.69266018
Maximum99.99625741
Range99.99625741
Interquartile range (IQR)22.70192805

Descriptive statistics

Standard deviation18.45934372
Coefficient of variation (CV)1.304782298
Kurtosis4.208027365
Mean14.14745107
Median Absolute Deviation (MAD)7.017712508
Skewness1.868540685
Sum11699.94204
Variance340.7473707
MonotocityNot monotonic
2020-12-17T23:58:55.820800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0253.0%
 
0.00798555413310.1%
 
47.0471236210.1%
 
7.61140250210.1%
 
0.163767910110.1%
 
1.48490702310.1%
 
3.5593481710.1%
 
0.742324135310.1%
 
0.00575677068210.1%
 
8.11155131210.1%
 
Other values (793)79395.9%
 
ValueCountFrequency (%) 
0253.0%
 
0.000295851569310.1%
 
0.000481030890410.1%
 
0.000493142501310.1%
 
0.000495325892610.1%
 
ValueCountFrequency (%) 
99.9962574110.1%
 
99.9961382510.1%
 
99.9960143510.1%
 
99.9814059110.1%
 
99.9792440910.1%
 

circuit_pct
Real number (ℝ≥0)

ZEROS

Distinct232
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.3615233
Minimum0
Maximum100
Zeros451
Zeros (%)54.5%
Memory size6.5 KiB
2020-12-17T23:58:56.009963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q355
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)55

Descriptive statistics

Standard deviation37.81859357
Coefficient of variation (CV)1.491179892
Kurtosis-0.6091574692
Mean25.3615233
Median Absolute Deviation (MAD)0
Skewness1.056749176
Sum20973.97977
Variance1430.24602
MonotocityNot monotonic
2020-12-17T23:58:56.206359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
045154.5%
 
100779.3%
 
1.66666666770.8%
 
2560.7%
 
5040.5%
 
93.2584269740.5%
 
74.4186046540.5%
 
56.4102564140.5%
 
6030.4%
 
38.5542168730.4%
 
Other values (222)26431.9%
 
ValueCountFrequency (%) 
045154.5%
 
0.153778558910.1%
 
0.15421899110.1%
 
0.235294117620.2%
 
0.237053245820.2%
 
ValueCountFrequency (%) 
100779.3%
 
99.4594594610.1%
 
98.8047808810.1%
 
98.4415584410.1%
 
97.413793110.1%
 

Interactions

2020-12-17T23:57:23.123376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:23.447762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:23.818658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:24.191154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:24.438241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:24.850260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:25.386991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:25.566241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:25.743856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:25.923245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:26.098508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:26.317605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:26.495641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:26.673003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:26.842356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:27.017320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:27.188205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:27.388339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:27.622706image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:27.821568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:28.050245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:28.280712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:28.485855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:28.666341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:28.877975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:29.058270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:29.256936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:29.441595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:29.637438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:29.837669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:30.211709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:30.397949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:57:30.579740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-17T23:58:16.443531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:16.943073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:17.123515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:17.292511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:17.478705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:17.649364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:17.824751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:17.999435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:18.230901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:18.406275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:18.604217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:18.780072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:19.000379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:19.190496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:19.384972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:19.553681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:19.751452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:19.916061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:20.089926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:20.265019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:20.441530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:20.613998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:20.783687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:21.016423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:21.192873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:21.369913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:21.539392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:21.724400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:21.915959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:22.144335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:22.316394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:22.506945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:22.682272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:22.848173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:23.030588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:23.210885image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:23.379222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:23.598229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:23.796159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:23.984959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:24.181083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:24.348784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:24.554064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:24.718646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:24.890354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:25.074642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:25.252038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:25.442980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:25.624057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:25.791012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:25.961615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:26.144347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:26.345351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:26.513806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:26.684860image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:26.879119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:27.053734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:27.230755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:27.404172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:27.574360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:27.743390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:27.925909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:28.099783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:28.289975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:28.470018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:28.654004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:28.834324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:29.016781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:29.194082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:29.374655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:29.553905image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:29.726694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:29.903388image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:30.082923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:30.256106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:30.432996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:30.598648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:30.763994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:30.949486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:31.126231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:31.318433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:31.574736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:31.740711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:32.047398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:32.248150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:32.419956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:32.589848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:32.756982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:32.937175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:33.097577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:33.261355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:33.799646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:34.030690image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:34.224024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:34.394660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:34.580282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:34.758548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:35.274123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:35.444259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:35.635096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:35.828589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:36.001220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:36.180846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:36.364580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:36.600514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:36.793518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:36.970593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:37.151280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:37.332608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:37.558087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:37.885554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:38.564842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:39.184324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-17T23:58:56.425575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-17T23:58:56.787618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-17T23:58:57.175058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-17T23:58:57.613851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-17T23:58:57.999395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-17T23:58:39.746028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:41.224998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:41.590370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-17T23:58:41.776040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

yearnamestatetotal_mwhtotal_custnotypesaidi_nomedsaifi_nomedcaidi_nomedcircuitsvoltagenercgen_mwhpurchase_mwhpv_mwhwind_mwhnm_mwhee_mwhdem_res_customersdem_res_mwhpv_pctwind_pctnm_pctee_pctdem_res_cust_pctcircuit_pct
02019.0Allamakee-Clayton El Coop, IncIA133395.09987.0329.0Cooperative141.6000.965146.7357553.00.0MRO148.0150540.01035.7490.1441035.8931124.0002768.00.00.7764530.0001080.7765610.84261027.7160310.000000
12019.0City of Ames - (IA)IA639455.026956.0554.0Municipal15.7900.1983.10526341.00.0MRO126113.0663514.00.0000.0000.0004218.00010683.00.00.0000000.0000000.0000000.65962439.6312510.000000
22019.0City of Anaheim - (CA)CA2237118.0120279.0590.0Municipal42.1200.5773.894737123.0123.0WECC22353.03009839.00.0000.0000.00026739.84114.00.00.0000000.0000000.0000001.1952810.011640100.000000
32019.0Arizona Public Service CoAZ27844577.01260386.0803.0Investor Owned86.8000.843102.96561386.00.0WECC26805008.07156889.00.0000.0000.000143225.000693725.0977215.00.0000000.0000000.0000000.51437355.0406780.000000
42019.0Entergy Arkansas LLCAR21818109.0713072.0814.0Investor Owned308.8001.72179.534881255.00.0SERC27576017.04700126.00.0000.0000.000248663.00023369.00.00.0000000.0000000.0000001.1397093.2772290.000000
52019.0Atlantic City Electric CoNJ4889951.0486628.0963.0Investor Owned76.0000.8985.393258355.00.0RFC0.06521522.00.0000.0000.00013574.00043558.0612.00.0000000.0000000.0000000.2775908.9509850.000000
62019.0City of Austin - (MN)MN335157.011746.01009.0Municipal31.8850.231138.030326.00.0MRO0.0341870.025.0370.00025.0375596.2066809.0140.8820.0074700.0000000.0074701.66972757.9686700.000000
72019.0Austin EnergyTX13695862.0499542.01015.0Municipal68.6800.8778.942529404.075.0TRE9115458.05666762.00.0000.0000.000121336.32068343.086.920.0000000.0000000.0000000.88593413.68113218.564356
82019.0Baltimore Gas & Electric CoMD12506681.0965643.01167.0Investor Owned81.7810.8497.3583331566.0628.0RFC0.014754342.00.0000.0000.000459516.583758207.02871.2250.0000000.0000000.0000003.67416978.51835540.102171
92019.0Barron Electric CoopWI332559.018850.01251.0Cooperative144.7201.07135.2523437.00.0MRO122.0352789.00.0000.0000.0001144.0006371.00.00.0000000.0000000.0000000.34399933.7984080.000000

Last rows

yearnamestatetotal_mwhtotal_custnotypesaidi_nomedsaifi_nomedcaidi_nomedcircuitsvoltagenercgen_mwhpurchase_mwhpv_mwhwind_mwhnm_mwhee_mwhdem_res_customersdem_res_mwhpv_pctwind_pctnm_pctee_pctdem_res_cust_pctcircuit_pct
8172013.0Wake Electric Membership CorpNC674269.036896.019981.0Cooperative83.0001.30463.65030773.00.0SERC.709377.045.1450.00045.1452206.01.000.0066950.000000e+000.0066950.3271690.0027100.000000
8182013.0City of Waseca - (MN)MN61010.04148.020136.0Municipal30.0000.7738.9610395.0NaNMRO.64314.00.0000.0000.000445.01601.0350.0000000.000000e+000.0000000.72938938.5969140.000000
8192013.0West Penn Power CompanyPA7494953.0505069.020387.0Investor Owned151.0001.09138.53211830.0830.0RFC.7998118.00.0000.0000.000212827.00.0.0.0000000.000000e+000.0000002.8396040.000000100.000000
8202013.0Western Massachusetts Electric CompanyMA1822410.0187444.020455.0Investor Owned88.000188219.00.0NPCC50831944297.00.0000.0000.000110826.01.020.0000000.000000e+000.0000006.0812880.0005330.000000
8212013.0Withlacoochee River Elec CoopFL3565155.0202352.020885.0Cooperative115.0001.62170.943862181.00.0FRCC.3720951.00.0000.0000.00038.0202310.015320.0000000.000000e+000.0000000.00106699.9792440.000000
8222013.0Westar Energy IncKS9826375.0373094.022500.0Investor Owned101.9301.24481.937299905.0NaNSPP151751613283074.00.0000.0000.000885.029825.0.0.0000000.000000e+000.0000000.0090067.9939640.000000
8232013.0Tucson Electric Power CoAZ9278919.0409529.024211.0Investor Owned63.4990.68392.970717420.00.0WECC113111822659239.048733.7010.08048733.781138815.037.0.0.5252098.621694e-070.5252101.4960260.0090350.000000
8242013.0Cass County Elec Coop IncND1119192.039897.024949.0Cooperative46.0001.1141.44144139.010.0MRO.1158278.065.12960.513125.64234.09985.0183800.0058195.406847e-030.0112260.00303825.02694425.641026
8252013.0Western Indiana Energy REMCIN628689.016603.025295.0Cooperative238.010..68.00.0RFC.650274.014.4000.00014.4001214.0350.072880.0022900.000000e+000.0022900.1931002.1080530.000000
8262013.0Entergy Texas Inc.TX16813590.0421105.055937.0Investor Owned144.9001.32109.77273479.00.0SERC703378017919380.00.0000.0000.00030979.08.059660.0000000.000000e+000.0000000.1842500.0019000.000000